Facial Recognition for Car Security System Using Fisherface Method

Rahmat, R F; Loi, Michele; Faza, Sharfina; Arisandi, Dedy; Budiarto, Rahmat

doi:10.1088/1742-6596/1235/1/012119

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…More datasets are needed to verify the system. Rahmat et al [11] proposed a real-time facial recognition method named "Fisherface". The technique was very efficient and used to improve security regarding recognizing the car owner.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart and Secure Vehicle Parking System to Avoid Theft Using Deep Image Recognition

Saba Ramzan,

Muhammad Javaid Iqbal,

Farrukh Arslan

et al. 2024

JICET

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Secure intelligent parking systems are complicated issues around the world. A secure parking system that is larceny-free is considered a luxury now. Parking is expensive and limited in almost all large cities. This research study presents a unique, safe, intelligent parking system based on deep image recognition techniques. To solve this tedious problem, an object detection deep learning model such as You Only Look Once (YOLO) is used. The proposed model is divided into three steps. The first step captures the car's image while entering the parking area. The second step is License plate recognition, in which the license plate is detected and saved the individual characters, and the recognized dataset is used when the driver leaves the parking area. The third step is facing detection and recognition of the taken plate. The fourth step is that the printer generates an invoice code used in case of a mismatch in the face of the driver. Evaluation of the proposed model indicates its efficiency. Accuracy of the following modules is achieved for vehicle detection (99%), number plate detection and recognition 98%, and 95%, respectively, and face detection and recognition 99%, and 97%, respectively. The proposed model's efficiency is shown through its evaluation. To get more security, a two-way screening procedure is used to keep the vehicles secure. So, the identification system's unique idea is to achieve better results by using two cameras and two-way authentication. It also forbids the entrance of those vehicles and drivers who are unauthorized or not allowed by the firm. Moreover, these modules were successfully implemented and evaluated using various performance measures, including precision, recall, and mean average precision.

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Section: Literature Reviewmentioning

confidence: 99%

Smart and Secure Vehicle Parking System to Avoid Theft Using Deep Image Recognition

Saba Ramzan,

Muhammad Javaid Iqbal,

Farrukh Arslan

et al. 2024

JICET

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“…We have used PCA with advanced features that can outperform many other methods when the database is a little smaller. For the comparative analysis, we selected three OpenCV-based FR methods from the literature that are used to verify the authenticity of the user [10], [18] and [19]. The results of the analysis are shown graphically in Fig.…”

Section: Comparison With Existing Approaches In Literaturementioning

confidence: 99%

Cost Efficient Automobile Security Using Face Recognition and GSM Module

Gulzar

Qadeer

Aamir

et al. 2022

JCBI

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Face detection is one of the most important modern computer vision topics. The importance of face recognition (FR) is increasing in our society for identification purposes. In this paper, we practically demonstrated the authenticity of an automatic security system for automobiles using face recognition technology. Our proposed system is not only a facial recognition system, but also supports authentication via a GSM module for remote monitoring and permission/control by a specific remote security code. The system was implemented and tested using a Raspberry Pi 3 board, Python and OpenCV to program the different modules for face recognition. The implementation results confirm the applicability of the proposed system as it offers the advantages of robustness, high accuracy, reliability and security at the lowest cost. This paper defines a combination of fail-safe algorithms for the GPS/GSM module in addition to face recognition algorithms, which are more suitable in dynamic environments where high uncertainties are expected and require high accuracy and reasonable speed. In future, we aim to develop an intelligent smart home application to meet the upcoming needs.

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